Compute seed-based time-frequency connectivity in sensor space.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Compute seed-based time-frequency connectivity in sensor space\n",
    "- Computes the connectivity between a seed-gradiometer close to the visual cortex and all other gradiometers.\n",
    "- The connectivity is computed in the time-frequency domain using Morlet wavelets and debiased Squared Weighted Phase Lag Index[1] is used as connectivity metric\n",
    "\n",
    "[1] Vinck et al. “An improved index of phase-synchronization for electro- physiological data in the presence of volume-conduction, noise and sample-size bias” NeuroImage, vol. 55, no. 4, pp. 1548-1565, Apr. 2011."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import mne\n",
    "from mne import io\n",
    "from mne.connectivity import spectral_connectivity,seed_target_indices\n",
    "from mne.datasets import sample\n",
    "from mne.time_frequency import AverageTFR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_path = sample.data_path()\n",
    "raw_fname = data_path + '/MEG/sample/sample_audvis_filt-0-40_raw.fif'\n",
    "event_fname = data_path + '/MEG/sample/sample_audvis_filt-0-40_raw-eve.fif'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Opening raw data file /home/reallocing/mne_data/MNE-sample-data/MEG/sample/sample_audvis_filt-0-40_raw.fif...\n",
      "    Read a total of 4 projection items:\n",
      "        PCA-v1 (1 x 102)  idle\n",
      "        PCA-v2 (1 x 102)  idle\n",
      "        PCA-v3 (1 x 102)  idle\n",
      "        Average EEG reference (1 x 60)  idle\n",
      "    Range : 6450 ... 48149 =     42.956 ...   320.665 secs\n",
      "Ready.\n",
      "Current compensation grade : 0\n"
     ]
    }
   ],
   "source": [
    "# Load the raw data\n",
    "raw = io.read_raw_fif(raw_fname)\n",
    "events = mne.read_events(event_fname)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Add a bad channel\n",
    "raw.info['bads'] += ['MEG 2443']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Pick MEG gradiometers\n",
    "picks = mne.pick_types(raw.info,meg='grad',eeg=False,stim=False,eog=True,exclude='bads')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[6994,    0,    2],\n",
       "       [7086,    0,    3],\n",
       "       [7192,    0,    1],\n",
       "       [7304,    0,    4],\n",
       "       [7413,    0,    2],\n",
       "       [7506,    0,    3],\n",
       "       [7612,    0,    1],\n",
       "       [7709,    0,    4],\n",
       "       [7810,    0,    2],\n",
       "       [7916,    0,    3],\n",
       "       [8025,    0,    1],\n",
       "       [8129,    0,    4],\n",
       "       [8233,    0,    2],\n",
       "       [8331,    0,    3],\n",
       "       [8428,    0,    1],\n",
       "       [8522,    0,    5],\n",
       "       [8633,    0,    2],\n",
       "       [8662,    0,   32],\n",
       "       [8739,    0,    3],\n",
       "       [8857,    0,    1]])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 打印前20行的events\n",
    "events[0:20,...]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "73 matching events found\n",
      "Applying baseline correction (mode: mean)\n",
      "Not setting metadata\n",
      "4 projection items activated\n",
      "Loading data for 73 events and 106 original time points ...\n",
      "    Rejecting  epoch based on EOG : [u'EOG 061']\n",
      "    Rejecting  epoch based on EOG : [u'EOG 061']\n",
      "    Rejecting  epoch based on EOG : [u'EOG 061']\n",
      "    Rejecting  epoch based on EOG : [u'EOG 061']\n",
      "    Rejecting  epoch based on EOG : [u'EOG 061']\n",
      "    Rejecting  epoch based on EOG : [u'EOG 061']\n",
      "6 bad epochs dropped\n"
     ]
    }
   ],
   "source": [
    "# Create epochs for left-visual condition\n",
    "event_id = 3\n",
    "tmin = -0.2\n",
    "tmax = 0.5\n",
    "reject = dict(grad=4000e-13,eog=150e-6)\n",
    "epochs = mne.Epochs(raw,events,event_id,tmin,tmax,picks=picks,baseline=(None,0),\n",
    "                   reject=reject,preload=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Use 'MEG 2343' as seed\n",
    "seed_ch = 'MEG 2343'\n",
    "pick_ch_names = [raw.ch_names[i] for i in picks]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "u'MEG 0113'"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 查看raw.ch_names的值\n",
    "raw.ch_names[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "178"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create seed-target indices for connectivity computation\n",
    "seed = pick_ch_names.index(seed_ch)\n",
    "seed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,  10,  11,  12,\n",
       "        13,  14,  15,  16,  17,  18,  19,  20,  21,  22,  23,  24,  25,\n",
       "        26,  27,  28,  29,  30,  31,  32,  33,  34,  35,  36,  37,  38,\n",
       "        39,  40,  41,  42,  43,  44,  45,  46,  47,  48,  49,  50,  51,\n",
       "        52,  53,  54,  55,  56,  57,  58,  59,  60,  61,  62,  63,  64,\n",
       "        65,  66,  67,  68,  69,  70,  71,  72,  73,  74,  75,  76,  77,\n",
       "        78,  79,  80,  81,  82,  83,  84,  85,  86,  87,  88,  89,  90,\n",
       "        91,  92,  93,  94,  95,  96,  97,  98,  99, 100, 101, 102, 103,\n",
       "       104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116,\n",
       "       117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129,\n",
       "       130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142,\n",
       "       143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155,\n",
       "       156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168,\n",
       "       169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181,\n",
       "       182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194,\n",
       "       195, 196, 197, 198, 199, 200, 201, 202, 203])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "targets = np.arange(len(picks))\n",
    "targets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('indices', (array([178, 178, 178, 178, 178, 178, 178, 178, 178, 178, 178, 178, 178,\n",
      "       178, 178, 178, 178, 178, 178, 178, 178, 178, 178, 178, 178, 178,\n",
      "       178, 178, 178, 178, 178, 178, 178, 178, 178, 178, 178, 178, 178,\n",
      "       178, 178, 178, 178, 178, 178, 178, 178, 178, 178, 178, 178, 178,\n",
      "       178, 178, 178, 178, 178, 178, 178, 178, 178, 178, 178, 178, 178,\n",
      "       178, 178, 178, 178, 178, 178, 178, 178, 178, 178, 178, 178, 178,\n",
      "       178, 178, 178, 178, 178, 178, 178, 178, 178, 178, 178, 178, 178,\n",
      "       178, 178, 178, 178, 178, 178, 178, 178, 178, 178, 178, 178, 178,\n",
      "       178, 178, 178, 178, 178, 178, 178, 178, 178, 178, 178, 178, 178,\n",
      "       178, 178, 178, 178, 178, 178, 178, 178, 178, 178, 178, 178, 178,\n",
      "       178, 178, 178, 178, 178, 178, 178, 178, 178, 178, 178, 178, 178,\n",
      "       178, 178, 178, 178, 178, 178, 178, 178, 178, 178, 178, 178, 178,\n",
      "       178, 178, 178, 178, 178, 178, 178, 178, 178, 178, 178, 178, 178,\n",
      "       178, 178, 178, 178, 178, 178, 178, 178, 178, 178, 178, 178, 178,\n",
      "       178, 178, 178, 178, 178, 178, 178, 178, 178, 178, 178, 178, 178,\n",
      "       178, 178, 178, 178, 178, 178, 178, 178, 178]), array([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,  10,  11,  12,\n",
      "        13,  14,  15,  16,  17,  18,  19,  20,  21,  22,  23,  24,  25,\n",
      "        26,  27,  28,  29,  30,  31,  32,  33,  34,  35,  36,  37,  38,\n",
      "        39,  40,  41,  42,  43,  44,  45,  46,  47,  48,  49,  50,  51,\n",
      "        52,  53,  54,  55,  56,  57,  58,  59,  60,  61,  62,  63,  64,\n",
      "        65,  66,  67,  68,  69,  70,  71,  72,  73,  74,  75,  76,  77,\n",
      "        78,  79,  80,  81,  82,  83,  84,  85,  86,  87,  88,  89,  90,\n",
      "        91,  92,  93,  94,  95,  96,  97,  98,  99, 100, 101, 102, 103,\n",
      "       104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116,\n",
      "       117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129,\n",
      "       130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142,\n",
      "       143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155,\n",
      "       156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168,\n",
      "       169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181,\n",
      "       182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194,\n",
      "       195, 196, 197, 198, 199, 200, 201, 202, 203])))\n"
     ]
    }
   ],
   "source": [
    "indices = seed_target_indices(seed,targets)\n",
    "print(\"indices\",indices)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 7,  9, 11, 13, 15, 17, 19, 21, 23, 25, 27, 29])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Define wavelet frequencies and number of cycles\n",
    "cwt_freqs = np.arange(7,30,2)\n",
    "cwt_freqs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1.        ,  1.28571429,  1.57142857,  1.85714286,  2.14285714,\n",
       "        2.42857143,  2.71428571,  3.        ,  3.28571429,  3.57142857,\n",
       "        3.85714286,  4.14285714])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cwt_n_cycles = cwt_freqs / 7.\n",
    "cwt_n_cycles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Connectivity computation...\n",
      "    computing connectivity for 204 connections\n",
      "    using t=0.000s..0.699s for estimation (106 points)\n",
      "    frequencies: 9.0Hz..29.0Hz (11 points)\n",
      "    using CWT with Morlet wavelets to estimate spectra\n",
      "    the following metrics will be computed: Debiased WPLI Square\n",
      "    computing connectivity for epochs 1..2\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=2)]: Using backend LokyBackend with 2 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    computing connectivity for epochs 3..4\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=2)]: Done   2 out of   2 | elapsed:    0.9s remaining:    0.0s\n",
      "[Parallel(n_jobs=2)]: Done   2 out of   2 | elapsed:    0.9s finished\n",
      "[Parallel(n_jobs=2)]: Using backend LokyBackend with 2 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    computing connectivity for epochs 5..6\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=2)]: Done   2 out of   2 | elapsed:    0.6s remaining:    0.0s\n",
      "[Parallel(n_jobs=2)]: Done   2 out of   2 | elapsed:    0.6s finished\n",
      "[Parallel(n_jobs=2)]: Using backend LokyBackend with 2 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    computing connectivity for epochs 7..8\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=2)]: Done   2 out of   2 | elapsed:    0.7s remaining:    0.0s\n",
      "[Parallel(n_jobs=2)]: Done   2 out of   2 | elapsed:    0.7s finished\n",
      "[Parallel(n_jobs=2)]: Using backend LokyBackend with 2 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    computing connectivity for epochs 9..10\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=2)]: Done   2 out of   2 | elapsed:    0.7s remaining:    0.0s\n",
      "[Parallel(n_jobs=2)]: Done   2 out of   2 | elapsed:    0.7s finished\n",
      "[Parallel(n_jobs=2)]: Using backend LokyBackend with 2 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    computing connectivity for epochs 11..12\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=2)]: Done   2 out of   2 | elapsed:    0.7s remaining:    0.0s\n",
      "[Parallel(n_jobs=2)]: Done   2 out of   2 | elapsed:    0.7s finished\n",
      "[Parallel(n_jobs=2)]: Using backend LokyBackend with 2 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    computing connectivity for epochs 13..14\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=2)]: Done   2 out of   2 | elapsed:    0.7s remaining:    0.0s\n",
      "[Parallel(n_jobs=2)]: Done   2 out of   2 | elapsed:    0.7s finished\n",
      "[Parallel(n_jobs=2)]: Using backend LokyBackend with 2 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    computing connectivity for epochs 15..16\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=2)]: Done   2 out of   2 | elapsed:    0.6s remaining:    0.0s\n",
      "[Parallel(n_jobs=2)]: Done   2 out of   2 | elapsed:    0.6s finished\n",
      "[Parallel(n_jobs=2)]: Using backend LokyBackend with 2 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    computing connectivity for epochs 17..18\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=2)]: Done   2 out of   2 | elapsed:    0.6s remaining:    0.0s\n",
      "[Parallel(n_jobs=2)]: Done   2 out of   2 | elapsed:    0.6s finished\n",
      "[Parallel(n_jobs=2)]: Using backend LokyBackend with 2 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    computing connectivity for epochs 19..20\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=2)]: Done   2 out of   2 | elapsed:    0.6s remaining:    0.0s\n",
      "[Parallel(n_jobs=2)]: Done   2 out of   2 | elapsed:    0.6s finished\n",
      "[Parallel(n_jobs=2)]: Using backend LokyBackend with 2 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    computing connectivity for epochs 21..22\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=2)]: Done   2 out of   2 | elapsed:    0.6s remaining:    0.0s\n",
      "[Parallel(n_jobs=2)]: Done   2 out of   2 | elapsed:    0.6s finished\n",
      "[Parallel(n_jobs=2)]: Using backend LokyBackend with 2 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    computing connectivity for epochs 23..24\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=2)]: Done   2 out of   2 | elapsed:    0.8s remaining:    0.0s\n",
      "[Parallel(n_jobs=2)]: Done   2 out of   2 | elapsed:    0.8s finished\n",
      "[Parallel(n_jobs=2)]: Using backend LokyBackend with 2 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    computing connectivity for epochs 25..26\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=2)]: Done   2 out of   2 | elapsed:    0.7s remaining:    0.0s\n",
      "[Parallel(n_jobs=2)]: Done   2 out of   2 | elapsed:    0.7s finished\n",
      "[Parallel(n_jobs=2)]: Using backend LokyBackend with 2 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    computing connectivity for epochs 27..28\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=2)]: Done   2 out of   2 | elapsed:    0.7s remaining:    0.0s\n",
      "[Parallel(n_jobs=2)]: Done   2 out of   2 | elapsed:    0.7s finished\n",
      "[Parallel(n_jobs=2)]: Using backend LokyBackend with 2 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    computing connectivity for epochs 29..30\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=2)]: Done   2 out of   2 | elapsed:    0.6s remaining:    0.0s\n",
      "[Parallel(n_jobs=2)]: Done   2 out of   2 | elapsed:    0.6s finished\n",
      "[Parallel(n_jobs=2)]: Using backend LokyBackend with 2 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    computing connectivity for epochs 31..32\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=2)]: Done   2 out of   2 | elapsed:    0.7s remaining:    0.0s\n",
      "[Parallel(n_jobs=2)]: Done   2 out of   2 | elapsed:    0.7s finished\n",
      "[Parallel(n_jobs=2)]: Using backend LokyBackend with 2 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    computing connectivity for epochs 33..34\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=2)]: Done   2 out of   2 | elapsed:    0.7s remaining:    0.0s\n",
      "[Parallel(n_jobs=2)]: Done   2 out of   2 | elapsed:    0.7s finished\n",
      "[Parallel(n_jobs=2)]: Using backend LokyBackend with 2 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    computing connectivity for epochs 35..36\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=2)]: Done   2 out of   2 | elapsed:    0.6s remaining:    0.0s\n",
      "[Parallel(n_jobs=2)]: Done   2 out of   2 | elapsed:    0.6s finished\n",
      "[Parallel(n_jobs=2)]: Using backend LokyBackend with 2 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    computing connectivity for epochs 37..38\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=2)]: Done   2 out of   2 | elapsed:    0.7s remaining:    0.0s\n",
      "[Parallel(n_jobs=2)]: Done   2 out of   2 | elapsed:    0.7s finished\n",
      "[Parallel(n_jobs=2)]: Using backend LokyBackend with 2 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    computing connectivity for epochs 39..40\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=2)]: Done   2 out of   2 | elapsed:    0.6s remaining:    0.0s\n",
      "[Parallel(n_jobs=2)]: Done   2 out of   2 | elapsed:    0.6s finished\n",
      "[Parallel(n_jobs=2)]: Using backend LokyBackend with 2 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    computing connectivity for epochs 41..42\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=2)]: Done   2 out of   2 | elapsed:    0.6s remaining:    0.0s\n",
      "[Parallel(n_jobs=2)]: Done   2 out of   2 | elapsed:    0.6s finished\n",
      "[Parallel(n_jobs=2)]: Using backend LokyBackend with 2 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    computing connectivity for epochs 43..44\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=2)]: Done   2 out of   2 | elapsed:    0.6s remaining:    0.0s\n",
      "[Parallel(n_jobs=2)]: Done   2 out of   2 | elapsed:    0.6s finished\n",
      "[Parallel(n_jobs=2)]: Using backend LokyBackend with 2 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    computing connectivity for epochs 45..46\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=2)]: Done   2 out of   2 | elapsed:    0.6s remaining:    0.0s\n",
      "[Parallel(n_jobs=2)]: Done   2 out of   2 | elapsed:    0.6s finished\n",
      "[Parallel(n_jobs=2)]: Using backend LokyBackend with 2 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    computing connectivity for epochs 47..48\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=2)]: Done   2 out of   2 | elapsed:    0.7s remaining:    0.0s\n",
      "[Parallel(n_jobs=2)]: Done   2 out of   2 | elapsed:    0.7s finished\n",
      "[Parallel(n_jobs=2)]: Using backend LokyBackend with 2 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    computing connectivity for epochs 49..50\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=2)]: Done   2 out of   2 | elapsed:    0.6s remaining:    0.0s\n",
      "[Parallel(n_jobs=2)]: Done   2 out of   2 | elapsed:    0.6s finished\n",
      "[Parallel(n_jobs=2)]: Using backend LokyBackend with 2 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    computing connectivity for epochs 51..52\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=2)]: Done   2 out of   2 | elapsed:    0.6s remaining:    0.0s\n",
      "[Parallel(n_jobs=2)]: Done   2 out of   2 | elapsed:    0.6s finished\n",
      "[Parallel(n_jobs=2)]: Using backend LokyBackend with 2 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    computing connectivity for epochs 53..54\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=2)]: Done   2 out of   2 | elapsed:    0.6s remaining:    0.0s\n",
      "[Parallel(n_jobs=2)]: Done   2 out of   2 | elapsed:    0.6s finished\n",
      "[Parallel(n_jobs=2)]: Using backend LokyBackend with 2 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    computing connectivity for epochs 55..56\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=2)]: Done   2 out of   2 | elapsed:    0.6s remaining:    0.0s\n",
      "[Parallel(n_jobs=2)]: Done   2 out of   2 | elapsed:    0.6s finished\n",
      "[Parallel(n_jobs=2)]: Using backend LokyBackend with 2 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    computing connectivity for epochs 57..58\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=2)]: Done   2 out of   2 | elapsed:    0.6s remaining:    0.0s\n",
      "[Parallel(n_jobs=2)]: Done   2 out of   2 | elapsed:    0.6s finished\n",
      "[Parallel(n_jobs=2)]: Using backend LokyBackend with 2 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    computing connectivity for epochs 59..60\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=2)]: Done   2 out of   2 | elapsed:    0.6s remaining:    0.0s\n",
      "[Parallel(n_jobs=2)]: Done   2 out of   2 | elapsed:    0.6s finished\n",
      "[Parallel(n_jobs=2)]: Using backend LokyBackend with 2 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    computing connectivity for epochs 61..62\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=2)]: Done   2 out of   2 | elapsed:    0.7s remaining:    0.0s\n",
      "[Parallel(n_jobs=2)]: Done   2 out of   2 | elapsed:    0.7s finished\n",
      "[Parallel(n_jobs=2)]: Using backend LokyBackend with 2 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    computing connectivity for epochs 63..64\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=2)]: Done   2 out of   2 | elapsed:    0.6s remaining:    0.0s\n",
      "[Parallel(n_jobs=2)]: Done   2 out of   2 | elapsed:    0.6s finished\n",
      "[Parallel(n_jobs=2)]: Using backend LokyBackend with 2 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    computing connectivity for epochs 65..66\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=2)]: Done   2 out of   2 | elapsed:    0.6s remaining:    0.0s\n",
      "[Parallel(n_jobs=2)]: Done   2 out of   2 | elapsed:    0.6s finished\n",
      "[Parallel(n_jobs=2)]: Using backend LokyBackend with 2 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    computing connectivity for epochs 67..67\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=2)]: Done   2 out of   2 | elapsed:    0.6s remaining:    0.0s\n",
      "[Parallel(n_jobs=2)]: Done   2 out of   2 | elapsed:    0.6s finished\n",
      "[Parallel(n_jobs=2)]: Using backend LokyBackend with 2 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Connectivity computation done]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=2)]: Done   1 out of   1 | elapsed:    0.6s finished\n"
     ]
    }
   ],
   "source": [
    "# Run the connectivity analysis using 2 parallel jobs\n",
    "sfreq = raw.info['sfreq'] # 采样频率\n",
    "con,freqs,times,_,_ = spectral_connectivity(epochs,indices=indices,method='wpli2_debiased', mode='cwt_morlet', sfreq=sfreq,\n",
    "                                           cwt_freqs=cwt_freqs, cwt_n_cycles=cwt_n_cycles, n_jobs=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Mark the seed channel with a value of 1.0, so we can see it in the plot\n",
    "con[np.where(indices[1] == seed)] = 1.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(204, 11, 106)"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "con.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(106,)"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "times.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Show topography of connectivity from seed\n",
    "title = 'WPLI2 - Visual - Seed %s' % seed_ch\n",
    "\n",
    "layout = mne.find_layout(epochs.info, 'meg')  # use full layout"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No baseline correction applied\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f17385412d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# AverageTFR:Container for Time-Frequency data.\n",
    "# - https://mne-tools.github.io/stable/generated/mne.time_frequency.AverageTFR.html#mne.time_frequency.AverageTFR\n",
    "tfr = AverageTFR(epochs.info,con,times,freqs,len(epochs))\n",
    "tfr.plot_topo(fig_facecolor='w', font_color='k', border='k');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No baseline correction applied\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f173a02d550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tfr.plot_topomap(tmin=tmin,tmax=tmax,ch_type='grad',mode='zscore');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Applying baseline correction (mode: mean)\n"
     ]
    },
    {
     "data": {
      "image/png": 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Ap7/3lFbaxEBZAZRhIJ6DOAoF+g0h4v+QtHYXCISZ9tjfI4PHH3fs6oJ1c4CHfo17bQa27XGKyAx0pF0afXWrlVK9NrPvhegMmDA6uOnsLSVfbOW8A4FH0AnQC4A/KKW+9b87CTgPHdZaDjwHXO675LfvPL+mpBaR7IhtLbjjyNEtjxk+QB6fNZ9O7VozbvQ+mLFsJNYMI5KJF4yyrqyGm+6+j8uuvQEjkknSVTiewhQhaAkhU7BNg+XLlvLN13M4YdLvtGT2fAnpub5H0I/ZUL4TxXNAeTz3+tvsP2wv8nKb16kTda7xWg3ZVzVUrR5dh9p7JPUqiekTSCn9ZvDngvLq1RCROlVGmQGWrFjD3mMnxqura0YppWbv4Hutiu5pHAjX4vw7NympReQjdMu6xzf+bqP9xgJPoFO6CtBJA18qpa74GfOzgaXAncADwFnAn4GuSilHRM4E5gGzgBboJIQXlVK3b++5ftWFYjRoP3H0Xr2bD+nbVaa8O5M+3ToyfvQ+GNEsJBLj6Xc+4ep7H6W4xmHK3fdzxQ23IpFMqtMecccj5SpcpfCUFpBKKQoLC2nRIreetIASA2XaKDvCvz6fzX1Pv4QXysQLZ+KFMqnxbO5/5hU++fcClB1GWUGUGWD6l19zz2PPbkRw0FK91uHiIV7t0Lqy1P54fHOglvoWyrB49d2POPvqW1H+Z709AKZNt27deeTv94QjkcgbfnLwDoVp243GVrAtaslvmdL1kFLqc6WUo5QqQGcwDd+GOf4HfjX1Q0QmZUcjY7OyMwOfrSrm+j8eSzgzCyMSQ8JRJBwjN78VZWnhroce46prrscIRqhOuyRdj7SrpbRniCY1Wl7OmTOb0076vVYLlEf9Qs8g6bi88a/pzFuwgKLyGlrk5tK3dw/27N+X99+cSsA0ULU2Zc+joKSUgqIS/kMFqVtA+ietJa9SiAiqjshmPfnFAFG0b9uGTu3a1llntFQ39ULUMDni8MN47a13M998+517gNN35D03Q9tl0rtFRG4FFgNXKaU+3sQ+OzOlayQ6pnu78auQWkRyQqHgw9Me+ktwSOd8Vq8rIpKfhxmOQiCEskOoQJixBx3E0HiK+x5/hoxm2VQkPV545il69N+TDl17gKGls1IK/LdoWWkpzZtlgZPwTwbKMHl/xqd8OGMGx/3+JHr17Y8YJusL17Ng/nf89YFHSSXiCNC+XRuOmDCe3Kwoxx19BMcffohWUfyFoqqV0obhW08UiFcf4IQgHigcQPHca28xdr+R5GRngQiDBvZj8IC+APpYhl6EOq7Hq9PeYMJh47jnL7eFP5z+0SQReUopNWNH3XdjG3VqtI68AEihe5y/ISIDlFI/bbTfTknpEpHTgEH8zB/9r0LqSDR6V5fOXTL67nsA8aoSJp4ymUvPOoVJR4zXaoJpgxlAmTZLF/5An759dfCbUsz69GMSKYf2XXVShQKmv/cun374Hn+5807CoZBvZdAEU4bJ0y+8RNLxuOaGm0k4iqSncB1FKDuXgcPHMGjfMQRMwQIKVq/ixtvv4s5bb8SkRuvknuPrvla9pK6T2ArlCQ8/8zIbSku54pzTUQiiXKqq4tz36D+JZkSYcPABen8lWicHLan9RemPy1dy29330q1bVwYO6M/f/3pbxpnnXfS4iHT/uQuvjfHZ0jV8Ov/Hre63kT7/pIgchy70c99Gu/7mKV0iMhG4CdhfKbW1jPdNYoeTWkQGhDKiR1144x3mhdfdzuUXnMNdd9zCoH59cCNR3/Jg1T1s0w6SSqW0cBS47f5HSKQ9PKXVDxFontuCvPyWxBOJuvNoVzY88s9niGZlM/G431GT9og7eoHpepD2FGnPQ4CgaRAwhexWbRmw52C+m7eAPTvm+S7zlCakXetR9Bd4AKJVjvwWOdhmA+mtFNFwmI9efZpAMFQXDFV/I4x6b6Rh0r1rVz5+63WisRh4HkccehB/e6B77sw53/wJndH+izFqYE9GDexZ9/nmlz/Y1v+qL/o/8ZumdInIwWjL0KFKqZ9dLmGHk9oIhO8cdNSZQSO/O3+45GqefP4JairKmDHrG4LBIM2zs+nfvz+DB+0JyqNPn968PPUNDj38SG2LFrAMwVVgGoIgDNhzEEOGDNbEDIaY/ukXfDXrSz6cPoPJl1/O0BGj6giddLw6nTzpeqQ97S20DSEjYJEZMEmlksQyIqB8W3YqjtZjQALw8lvvM+f7Bdx89WX6okQ4/KAx2nkDPrEBFAHbbhzw5O+/KbNhNCOiVR1AlMPfbr4mOmL8pOtF5BGlVPUvvfcSCG19H5EstJT9GHDQ6sK+aHPaxngS3ef8WWAdm0npAu5Fmwd7AmGl1JxNHGsGfkoXmrhn0SClS0TGAE8DE5VSX2/D5W4WO9T6ISJDFDLU6rKvuWhdJesdi9+dcS7nX3k9f/rzpZx8xtnsu/9BzJn7HU89+zyIiWWaWIaBctPYpmAZ+H8FQwSjdr2mwPUUfzrvAorLyigpq2DMgQcxeNgoqtKK6rRHdcrlpRee41/vvEVV2qUm7RJPu1RWx7nsjBOZ8f67eArWF64jv2UuS35YxlV/fQAnmdDk9oOcsrMyaV6XwKoJqWoXj/oToHyvpQe1i9aG5tE6s2C9GlOfIuYgTpr+3TpxwIi97WAgsClCbT+sQOOxadjogpnrgSLgHHRq1Q9+SlfFb5jSNbE2pQv9g8kE3vbTyipE5K2fdRt+zn/aLMzATVabvcLrCitZtraCdPEqQkP3xPEU0YBJ0AqRk9+GE04+jZuvv8bXQRUHjBnNu2+/xdjxE0mb2toB9W9zhda3XQVpTxg7fiIHjTuclKsoSTjE0x4JxyPpeCxf/hMZ0UwGezqc1DQEKxhkj6Ej6NqjDyKQiCfICIcpKi5hxZoC0uk0Zu0vBxgzYh/G7DeqgTah3xiIUGfX99UQUQqFb4Vp9BZvoJfX/Z96G7a4KXBS3HD+6ZEPPpt5mYjcqZRK/pLbvy2SWilVDAzZzHer2IiUSqm7gbs3s/864PhtnZ/vaNlkcq5Sasy2Hmdr2GGkFpFOGNYIFW0nZevLWLWmnNUrPyIgDsP33oucsE3UL1GgLINAMIhSmhTD9xrEn6+4hkPGTyBgCq7S3jlXNSC0p0njor9wPC2dCyoTzJ/3Pct/WErzNh0Ye8q5xAIWQcvAEMEUMEQ47az/wzQEs0Hd5uF7D2ZE366oZLWOIzGMOl1d1XoG9cWhDBPxfVu17vBav4aIH6KqjX31/w8aqyaeB67ru/HT4CTp06EVe/boLJ9/u/BY4J+/6BnsxkFMDbHj1A8xzzNyuouYNjUbCihau4rsPcbx8TvT+PrbeayvTlHqS9W0q/D8wHzxHEQ5nHT8MTz52D8I+J5D05C6yXkKHA/SLiRcjxrHozzp8uGnX3Db1Zcxe+580s3a8tXMmSxauBARyAxatIhY5EZsciMWzUIWUVsvFqU22F9MMG3Ef12rBnbnunhp/5PUqREN1JA69UN7MBsnDtTa0WuzZXQyrygXvLTvhUzhJRNccvxhsVgkdNkvfgSBUKOxu2KHkNqvXnmqkdMzCJCqKqXwsxcoLU/RfcIfefelp1n4w4/MX7SIN6e9juMpTZLah+257NG7B8WFhZQXrydggOWTS+vS4HiKlKeoSXmUxh3WVsSZ/fXXDDrxz0R6j6Q6ozVtRx5JVptOGCJk2AbNQhZORTHJknVkWoqsoEnYBNd16gKdsAKIHcSwA7oITu1JPc+3WmuiqlqXt+9GV1I///8ksKoj/IKFi5g5e06dLo2b1haXdBKVSqLSKQ7csxe2aXQQkb6/6DlYgUZjd8WOUj/GEIwZEsoCwEslUHaU4hXLaJ7bj/5Hn8NbzzxI+3ZtWb3gGyZOPAIxDFzHJVldze133s25Z5zGGSefwAsvvcBpZ53LK88/zffz5nHh1Tfi+a5yBdSkXMqTadZVpeg4+kh+Wl9NaVUST0FWRgAnV2EZEK8o5a6/3EyPrp0JBYOsXrMGV0F5ZSWHjx+v1QfDBMv2padvsQCfpEa95K2V0EpL6ydenMaCJcu4/coL/cvXk3v4mRcZOXQvenTv5seEeDz1/MuUlJaxz4A+4PqZ6k6qjtCkU4jrMLRnp8D0uYtPRcdD/CxIcPeVzg2xY0htWL83mnfLAFCei7dhKUaX4ZTM+5jS7t2JhW3aDhjOqP3H0CH7bBxPkZefz+q1awm4NSxcvJTi4vX07NOXstJSBEWHjp0oragi5ZvnHE9RVlHOX668iP2O+yNmfldWltRQXBanqiaFIUIwYCJAwDR4/vFHOGDEUAb36kLbljlawtpBHMPmmRdfo7pfT6LhgI7NMG0aLfL89C3HSfOnydcyaeI49t93mLZPY9C5fTviyQZrOt+m/fGXswmFQj6pPcDgxssuxHUcX/1wwU3jpfUiUaVTKP9vXlbUNA3jBBG5+OeGpu7OKkdD/GJSi4gg5ngjq50B6LjldA1g4lSXk6xOkUi5dOu/N4YfrO94in4DB/HVnK855pAxvPTY/YB2h+vYCthr6DB6Dtqbkpo0NWmPlOtR4xrE8tqSsqNUViQoqUxRE3dY++mrxAuWMPycG7FNIWAKqXgNTzz1FEv692DyyUeDaWKEoxSWVPLAw/+ge8c27DNoYF2M9e0PPMHqdUXce8PldddmGgat8vNokZvT6JpH7jOEkUM3WsSLwTP3/UX/W3laPVEelgGWJTrO29WJDcp1tJR2kr4p0SE3M0LANmPVSbqj4zG2H0ZT4i3sGEk9ADNgSTCrwSahNgzWcz0MwDTA8I3OCujaoxcfvj2V3407UMcde65v9wV864fnQdzxmDfve+Z/8zVDx09i1MkXUFiRpHhDNVVVSZKJNJld9iSa24KMoEXQMqkoWU+b1vlcc9oUwolyvIoNgMJIJWib1YxPX/0ngVBYZ5X7JrZR+wxmTWExDaW1iMH1ky+o+6w9jXo1wCarjdWa9dDXgvITBjRxleeAk67LilGOTmaoiseJBoOM27On8eync8fxM0mt3zhN2BGkHimZbTc6jn6wZrQFhmkQtA2ClontZ68IgpgmruvrtQ0kjFJeA7u0YvWqVXw47VWGH3cWhZUJKhIORRUJNlQkSdSk8DxFTvsu5OcNILXyOzJ67s/a5T/Ss3s3oraBqkzixatQrguOo9WUjCwdyqPcuqD+vffoB9IgG6YRfLIKjWM7NgVF3eKyNiMGJwXpZL2UdtI4ySSk0xiex7zlBfTr0JL22dHQ2/9eNA4dc7zdUGaTpIYdYf0wAgcYGS3rlDnluahkJYmFU4l1G4YVMAkHrbqKowHDwDRq+aED7pVp+/HHvgcO/bNwPHj7hacYfuwfKUsZFJTFKSxPUFyRoLIqRSrhEgha5OdmYK2Zi7tuKflZIWrKS8nLydEk8hydVuU6ePEqvJpKVLxKp2KlEnrxhqCM2jmYGxG7QdReQ4eKbw1Rhlk/arfX0lp5+vhOCpVM4CXiqGQclU5xwV2PceUjLwKKecvX0K9dS4Z0bUM8mR4s8jObIJqBxmM3xQ4w6XlDJKO+e5uKb0CiLbGyOxCMZROM2ETDNtGgRdg2CPm2YsOAnJwcijaU+cH0dn2UHNqr6LguroKUEWRDdZLC8iQlFUkqq1Ok0y6BkEm7vAzWTX+S1jGb8y78M61jQSpK1pPfsgVimIhpaRNX0LffIvX1PmqTC6yg71q2/Rjp+h9X47tVm5Drv13MjYZh+TEfDW3ZHjgOKp1EpRKolE4QPv7A4Ry731CU57G2pIxW2Zm0y8nCtkwLHZu83ahNTKhLUNgMRCRbRF4TkSoR+cmP0tvcvjusQ9fPOcf2zLUW201qv3bw5SIyVUSOwXNzCNZ7ViXcHKN5NzK6jiIzlkHbYJyVH79M84hNxDYJmULANLANoXXr1qxbX9SAIHad6U4pxdo1a8hqkU9N0qWiJk15VZKq6iSppItlGuTmRKj6/j2GjxzJERMm0CoawCkvpmDlT+RlZ4EdQEIRjEhUly2LZuoEhYCf/RII62ETBv/DAAAgAElEQVSHUFYI5YfDKtMnp2E04HZ9gm6tZMawGg+zNtulltiqXuVwHW3pcNLgeQwb0JuBPTrr9YRSWvgjdM/PcYADReQVEblbRLaZ4LVhvXXhvZvH/UACnTZ1IvCA6CZQGz/rHdKhS0Q6iMjGsdrbeo5tmmtD/BxJfRVwEDANeAArnKirboS2fohhYpgGAbeStR+/yImnnErbrBDZIS2tg6ZgG0IyGccyTT+Hz2bG51/Sf0B/lC+pDdum59BRJB2XeNIlEU+TTDgopYhEbJoH09jpKvYfsx85EYtY0OTZxx7mhquvxFAuf33seRas2YDEmiOxbCQzFy+jGXc+M5V1VUlUOBMViqECGZrUVhBl6dQrZViawP7isM7hIoZeJNapHVad2lKfvmVqR43vxFG1eZOu/1dEvzmCYf3DE/F9Noq+bVuEgcuBJUAAeFYa3uAtYeM3xybg1x85Ep3tEldKfY7uOfn7Tey+I9O5Nmem3Ow5tnOuddguUotIa+BPwAlKqUeBL0FtUnkTQ6j8aTZHnfJHeuc3I7FqMSXL5lG8ejnLFs7jvbfeYPbMmfTu3atOP5325juMO+xwnnzicb6eNZNY81xiOfmkHI9U2mXJS7dT/N0nmLZJLBakQ7tW/O70c3jl0fv49quZ2AYUFKxl+coVuGIwd/EyFheUQGYLaJYPzVpSqkJMnf45r8+YxYdz5vHmjJm8/fGXfL/0J86dfAUFRaVayhkWGCbrizdwyjkXsfTH5dRKawyTktIKrrv1r1THE/6+dmOpLcYmNRjEQKwgRli/PSQQJu24fpqZIpF2bXTw/FXAuejq+kdty/PRb5r6sRl0B9JKqWUNttWmVW2MPv53Dfdr6asEgk7n+gZoBewPnC8iB27LXLdyjjw/ZWx75lqH7V0uZ6ODxNf6nz8k0mI/tERpBKd8LSm7gtat2jDl4vMYv/8Igobip8oqItFMOnXqzM3XXcWCed/Rp3cfVq5aTd9+/UCEJYsWUpNyadV7T0xTMP1Gl8Gc9liZ+SjXN5sBGBZFhYWsKlhHRcrj8im38MmH7/HMi6/SplM35ixdxZwl//QtKmCYJkccczwZeXlUqgCR5s0wgH8++wILFi7GQTQpdSoOwVCQnObNycjIqNO3lZgUlmxg3oJFlFbVEMnM9q/ao7oiTqK6ktysSJ1El1rJaXn6cyQDwpmI8jBRtG2dz/zVRfTKa8bwbu1585vF8yoTKRdARGYDLbfp6Rjb1NQgSn1aVi021ylrR6VzbWnhu6lziH+O7ZlrHbaX1Am05KhFlhHO3qQbK7luAbRrS1nhGvbZey+6t82jT+scAoEAKhCCQISHnn2OJ559kY8/fI/MaAYrV6zAAG669XbKEi4FVSlsw8A2DSxTaLnP0ThpF8/zcF2PlONRlXI47qJraBayWF+VRswM9hp7BHsfLHVWFsdTpD1VF9vv4RNcIGQZZNgmlw3ai7/dfjO5LVrW26KBrMxM/nrjNf6n2pIHBr169+alF55DiUFVTQ2WZREKBrnxL3exeuVKnn7oXpTp6EVqIMS/5y2iX6d22KEQEslChTOpfSOfefwRXHPXw1x92DDat8jCNs3sBrcy6N/3reLjz2fyySefbG237emU9UvSuY5D68MK3V8mw9+31pjfXym1eivn+FldvX4OqRuQWDK3VLI+p1M/qiorCAUDnHbepVx22u845sB9kXQQ3BSnHHEoo0cMw1Ie2ZkZ9OjWhQ/e/xcjxxyEZYjvHTQIWgYBy/A9klqIOo5HddKhpDqFUrChKk6yvISyDSUUFawhmUggKAzTpv+osaRcpW3kPlvjVZXYhqJDq5bkRW3CtkHnrl358afl9O7WqT4oqe5Sa2uF1BLb4rVpbzFr9mxyc3Opicdx0mkG7jGIScccg7JCdUXcC9ev57Tr7uL2P5/J2P32hYwsvFCmjscWA8tzCITDbEi4xEJBaCyJQmwjqUeMHMWIkaPqPt90882b2m0JuoFRlwav9QFsOnP7l6RzPYcuSIPoni4fKaU6b+c5ktsx1zr8QlIT3tiz5q6bi5k/EIAWPQZjWS6GafHMfXfQLdPSZi1H17oLZVj06NAG5SZBWRx75OFccf2N7DNsBEE7TMQ2NLEtg6BtYtkGTlqz0nEVZTVplIKKeBqVrGTplx8QzmxOsFkL3FgOrgeEM/n0h5K6yA7TEIKWgSQr+ealBzjriilkh7VgLFi7lraHHOTXE/E9gtA4g8VXKZ576VUMy2LKTTfXcd910nz+2Wc8+s+nOffsM+neoQ0gtGzTgefvv4MendpDMIwTjHHrvQ/QMr8Vv5twCFmZLTjv9N9z+9//wZF92pF23YY1QbaZ1Glvc2uxeiilakTkVWCKiJwB7AkcBgzbxO47Mp1rcyrIZs+xnXOtw/ZaPxLo8lO1CLDxwryBdFMKIplZlFVU0LN7d6yArXnipiGV0LmBTlJX9XdTiJvk7NNP5r577yZoCiHL4Ov3pxKxDYIBEztgYdkmhghpx6W0KklheYLVpXGKUgGaDz4Uo9MQKjPas8HIZdnyQtaUpFm+upwVq8tZuaaC1eurWDxvPlWeTXabjnjptE7wBaoqK8mMRhuEkjZMyao3581fvJTC9es58qij8ZROZnCVthMPHTmaa2+8hedeeoV3Z3yGCkRQgTA9e/dFwpl4wRhPvfImg/YZwYC9hnLZ9TfzY3Gc7A7d6dK5EzNXFlGVTOc1uKMhYJsyYlxPNRpbwDlABJ2C9TRwllJq4a+ZzsVmrB9bOseW5rqli9teUm8sNZKb6mBVG2TmKUW4WXMK1xfruGXL1tVGwQ/q0YTGr+ovTooO+XkETIPKDcWELYNYKEjFykVkhW3CYYtg0MIwBcfxqIk7VMbTFK4toLColKLKJEUVSUrKExStL+X7525j5eyPqaxMUlWVoiaRpqayin8/dh1rvp5BqnIDWZkxAqYBnkfphhJ0BdPaFBefzA3MdxgmTz71DGee/SdchR8Wq6MIk64i6SiUaXPeJZczZ+53LPppla4KFcxAhaL8sKaIlWsLGTBkGC07dmXy9bdw54P/YGWVwym/P545K9eTFQ6ubnSPG69jNgvHU43G5qCUKlVKHaGUiiqlOiqlXvC3r1JKZfq6bu2+dyul8pVSzZRSf2hYzkEptU4pdbxSqpVSKkcpNUwpNX0T51uxGdVjW86xybluCdtL6hZAcYPPcTauTGsFwUlgZbbWEkMskmkHZfmF1Gs9b0idC1nctE5G9csV/G7ieF575UVClnDo+MNYOnM6ObEgTuEPrJ/5MqZl1F4wnqdY8PI9LH7jERy3tvCNwrTDdDrqarJ6DMM0DYJBk0jEpllOFvudezOtW+fRtm07cqJBMmyD996exrRp01hfWEjtglCJybR33ucfTz6jnS2mxfxFS+nTtx+mZdcaSHA9Rcr1SLqKhOPnS7oeZ/3f+Tz4yKN4VhAvkEFxVYp7HnqEs86/iOq0R0XSpdoz+dPkq7n53gepDDVn8B4DQaThTS327/tW4arGY3fF9urUuegMZB+qAielA4d9SDCGSlUR6joc19UZ3mlP4SCYpq1JXZf6p6uSKs8D8fTCCaFTm3xmzZrFpBNPITsaplksRjBdiVldhFNeSDgjUFcXJGAZ9DvyLEKRDKIZ2rKYTLtUxR2C4c5gCEHbJBqyyAxbNI8GiRkhln/xLqedcSYtowGyQiYZwQBTpkwhL7+Vn1WudeiisjKKN5SCqV3or7z2KmeffQ5vv/Um+wzfl3A0prNyXK3Tev5byhAIWDbjD5vAxZddSTQWQynF5CuuIYVBZTJNedLB8RQRy+QP51/GtX+9gwn77oVhPl3a4J4X+fd9q9gWnXp3wPaSemNJvU6lq+NARu0GCcTwqouomPsaxTk2P7V1CGVEKSmvorUV0PZa3yVMHcGVJreYoBTJRJzPv5jJ4488zElnncek35/Kww/dz4ij/kj34WOpSTl4SmEZ2ipituiOiNQtKg0RHNejJukSsAzCAZOMgEUsZNE8bJOTEWD8wLPJCdvEAtq6svfeQ7j9ttsoGXcoOdnZgKK4ZAMD9hzMhg2lPPXcC3z3/TxGjR6NHQhw6803cfHkyzho3GG+ZFR1Ge8Cdbr23sP3Zd+RI0k7DkpM4o5HSVWC6ydfRN8RB1JcUky3fnswsE8v9j/yWB5/6F6UGGsa3ONioNO2PJwtqRy7E36hpKaAVHXjklnBLMTOQFUswXUdln47hxH9ejF/8VJa9e+MmKYf72MgpoXjKr5dvIg9+vf3vXVCMBDkkfvvZdX6EgKm0KZlLm1atWLNnOl0GrI/KT/N3PJNfohQXlRAweIFFC5biIHCMATPVYw5YhId2nckYplEAyaxoEnUNigtXMXzT09j1cqVjBmzH+PGH8all13OI48/QTKRxPM8sps3p3379uTk5LDX0GFMOu4E1qxZzd/uuZvhw4YRCQUwRfn1SRSFBWsJZ0TJzMysL5rqqycYJmlXkXIVaQ+sUASJNicj2pKPP/yQL957m9P+73wqkg41ieSKBne0iM2UNNgY7nZXct418Usl9Y8qVdHoGDoRxiZzwESi+V1IVX9LXtt2rPzhW9ijuyazgQ76MU3+9fFMLr/tb7z/0pPk5uX5EXA2AwYMYO5zLxEwhWjA5MRTTuPd9z/AXbeULr0GIGhSm771ZeHyEnp0bMvh4w4lGg4RNIWF33zFnv26EQ0FCJrw48J5PP/OWxSsXUOPLp04/qgjaNW6Nfc++Ahz586lT/+BnHfhn+uNeQ1SFEWEFSuWc//f7uWWG64jZAovvDqVpx99mONPOwNPCbddezlt23dk8jVTdDZ8XQK6oPxSaI6nUGJywkXXsGR9JSUlNWTvPQHK1nDnlKsJ2pFUKpVq2Je7mCb1Y7vwcyR1w1fjUtLxsFIeG8fcGKaBSlWQnduCcCBASTqNEr9NRZ1IEcaOHkGHTl3IbZmvi0YaJsoK8OnMT+ndpw+WIYRtg+ywzbixB5FyvTq91RTBNAwMgU77j8I0wDZ0sFRRwRruuPLP3HTbHZQWFzJv7jcM6N2dyX84gSN+fzrd2+TSulkElU4wasQwlq1YQe9+A3FUbS1s/zpELxgKVq3koQfu47Yp1xAijcTjHH/IKD748mv+etMUzr/kMq6+9npimZlk2Dpm3BKpc/goX5ob/oLC8RQ1aY+y6jRFpTXYgVxaDD+K6bee5QINTVbbrFO7O7HR638TtpfUtj8AUEolxQwUkihvTTi70Y5uxRqcCpf2A/pRXrKe/JZ5VFaUc+Ot93DxqZNomZeLiGDaQXr17u2Hfwb5ccVqHn/yadp16MDJpxyGIAQMiAUMTAFFvbNHC8H60mS1xWssQ8jp3J5LLpnMh++8wbFHHcFJEw/GSNUgqTiP3XoVedkxjGQlCkXXDm15+/3pHDx+Ql31VaWoK4SzaN73vPjcM9x4xSWEvDiSqIR4JcpxOKB/Zzq1acndt0wBw8QwLZ1r6U/wiy9n0qFDR+68+x4sK4gfxqLNgK5HMuVQVZEkVbOBRHYWVWWl0JjUNpuIrdkUUk4TqWH7Sf0ycL+I3NYg43mWqik6QhqSWgzS6xeTynLo1uNoFr3/Cn/43WFUVxRRUFhERWUlLfNy6zoAYAVZUVDMQ4/9k/zWrbng4ksJZ2ToqgUobEOTyxIDQeG5LiJQVl7Gueecw8UXT2bQ4MFa9QFSyQRXX30VY0aP4q5bbtDOnVQNkk5AOk67nJiOa07FwbSJhrOorCgHz+Oic8/loEMOZewhh2KZwttTX2PpooXcds2lWKkqqCyDqjK8qjK8ZAIJhuiclcsNZ5/gR+r57nTLBivABSXF9O/fn+uuvZrrb7pV52o28K15rqJ06VyKv5+BchN4nptUSjV8G/6eehfyFpH2mpRq2H5Sf4KWHEOBL0UkCLR1S39MGTnddcdjz8ESj2jL9nTqP5jWOc2YVVJEq9zmWIEUT992JXXFFP046tLqBHf+/X6uu/EWTDuIq7Td1xBBDLj2qiuIZkRQflle09Qld1OpNFXlZUx95UU++Nc7iGHgODre+qILzqdtqzwknUTcNEY6qRNgnbTO4E6ntSPI0d1vR48YxqsvvUDHju3p0K4tG9av4x8P3sfwIYO58rw/IvEypLoUr7yYRfPm8dRbH5FOJomEQxw2cgh9e/XAsizENBA7gBltBpFM7rn2ElQgzLQPP2XmZ58wYJ+RmIbBT/PnYmR3RkSo+OlrMnofSsXC6XhOWolIO6XUKhEJo4ui77EtDyfpNJEatpPUSiklIg8DD4rIA+gwwzLiG9JKqYCI4K37FiOnLe0HjmXwsL2wUjU0z4phOHFwk9SV5/I8XXoLePCRx7l48qWIHSTp93kRwDJg3epVVFdV8t3cb5j2wtMELLO+GlKDNnHKsHANk3gyTUZGVGcJ1nYI8PMHDa++DRwobR/3HMRJcdDIobz14SfMLy/j3TenkhnL4LLzzqJZ0ESqSqBqA8niddzz1MtIIs65Q3oj8SQVqRRfzv6W1975WHvyDGHSAXszeK+9MBwHMhwEj3Fj9uWq2+9l0HCt+3/3+XR6HNYZyxTsSJaOpS5cEkd5HwNfiMj1wARgllJq5bY8n+1dKIrIycCjQA310XPjlVKbDPWTLXTX2l6ISAB4EB0rXg3coZS6y/+uG3AHOsbDAGYD5yullmzLsX9O+vHdwHJ0RsJnwDUodz3J8gxl65hjUxzadexA22ZhZk1/h3EH7Iek4pBKalJR26nWAdchlUrRIi+fqnR986JanfSbb75h/KGHsGblcoLiZ2bXVU6qbUBkgxiYYmFbFtdedx2xWIwzz/gDmZGQJnddB4M0biJBKpkibFo6+VYMDOUxftRQxu8/EiV+G7lUDVSUoCo2sGzRfO56eiqnDO5Fe8ugZnURqfI4nusx1LIZ1iqfQCxEKCfK3R/NJiMcoWfvnph4iGVjGjZB28Rz0tgitGrbnviGAoJ2jPxhR1H4ww+4G5YBXIxuDXEi8A6wVbdwLRI/T1J/oZQaubWd/LzB12ncXWuqiHRtUI53e3A9OnWrHdAa+EhE5iul3gOaoTNcTkGHmV7rf95iGlcttjudy++e9LLv879K6ZoGU73ylZ6qWIkRzSOrTQda52XSOhbgx4Xz6N+tAySrUamELubh15tTrou4jpaYfm+XeCpFaVmZH1cBS5csYeTwYZxxwjFaN/ZjRMSvSVffJQsQ4Zprr+PU006nYN06Ro7er869rfMHdZD/9Q8+yZk33KvzB9MJSFQhNeUYNWX+KEWqS6GyGFVayPvvf8gTr77L1fsNolUqTfnyIsqXl1D8YzHLF61j/aJCSpcVUblmA/GiCs7fpx8PvPY+61auxKsqh0Q14iTYe48BfPf1HGzToHf/gRQtm08kqAO13Mp1ABuUUsuUUi8qpSYopR7YnhYRaddrNHYwRrOF7lqicZmI/CAiRSLyvIg028LxTgKmKKUqlFKLgIfRJEYpNVsp9bhSqkzpDsF3AT38bJitYsdUPVXO017J0hoSZQSz29Ft+EG0z4uydNYMxo8dg5WuRpLVOqPa8xrFa4mXJhwKUFOt474fuudOLj/vbG22U5BMJgkFbN3sPp3QhPZbvslGxc6//vc37DloEG3btePsc/6PPQcNojqeqO9m64ePnnT4WM46ZpwfmJ1GJapRNeWomjLwiU1VCaq0iDfefZ9Fi5dw4bB+pApLqFxdQsXqUp7/dhlnfj2XP8+bz/0Ll/HqkpVUr68iXlyJU17F5FF7ctOTU0lXlOHVVEI6yfDBA5n15WfYptC7dx82rPyBjLBNIGyRWDHHQXlP/pLHkHS9RmMbsYefY7hIRK7aQj7klrprge5EMAHdlaA1urD6/Zs6kE/2Vhsdb0tpWqOAAqVU6Wa+b4QdVcr3Y9I1aSMYIa//aPp1b0+XnDDffvkJB+7VDypKqCherxdldbHK6L/KY8SQPfns4xnYhnDsiSfxh/+7QLfGEF3VSZSLOGlfUmtpjZvW+jnKD9yHdesK6dxZB4O1b9+BAw8ay/LlDZ1zghgm3Tt3YtigAQ1KG2j3POkUKlGNV12BqixnzYrlzPpuMSfv1ZtUeTXp6gTpmhTz1pfiJR3OirXiuEgeE4K5lMdT/FhcQaoySaqiBjuZYuKArrzxySxUvBpxkmRFAlSWlRK2DDJDNrZAZtAkFBAql36WxnOe+CUPIe2qRmObnhv0VUrloXXb44BLNrPv1rprnQlcqZQq8KPspgBHb+ZHEkWTYOM0rk116moL/B24cOPvNocdQmodW+s9bHiJRE40Qdn3M5jz5vOceOR4jMoiZs+axajTJ/PDTyvrym6htCVDlGLvgf34/LNPCVpCp3ZtGDFsKGFL18RDKQzlgZv262bEtRrjpLj/iWd5+qXXdYkBBdFoBlVVVXXzCofDJOI1+vbVLhgN3ddQ1wOx6/MHTZ0su+CH5Zx3633Ea2ooq6qmbYtszICNGbL1X9tkg+vQJhSiQyBMTzuCoBgTyGJ6STHpeJpUVZJ0dYJBrXKZs2g5KhnnyLMv5fGnnqNT+zas+WkZEdvgoAlHkmF6JJbNRoQftnUhtDl8N+szXnrgL3VjY4jI8Q1bTyilliulVvjPcD4+ETdz+K2lVnUAXhORDX7a1gJ04kBLEXmgwXkv848l/Gca18aduloA/wL+rpR6cVvvw44ruq68+1IF80ksfI+DDxhDvKSAod3a4m0ooE9ejMnHHEz7vGyU56E8Vdcf0VMeqUSc4UMGcddtt7Bh7QpCOLhVZTx+/73s0b+PX3nfLwiTTEBak9pzHDz/B4JStMrPp6CgQJc1E6GstJTs7Gxf8zDqGnQqw9SEtgJg2z659d+0p50iWCa9u3dhfXWSpRUJgs1iBLMyCMRCjGzXkk8TFXghg3DQJGSZhC2LhOtRGU/hJNJ4KQfluBgolJNi4uihjBzcn0kTDub1V18ibAkD+vcjPyeLpe88Wekma275pY+g8x5DOeT0C+rGfzwipZ5VSsX8mOlxmznM5jJU5qO7aTVEf3TrZYCVwCFKqeb+yFZKZfiS++wG571VKVUGrEWnZtWiUZqWr6L8C3hdKXXr1q69IXZY8TWl1Co7HP0oJywHv/f8I/KXi8/CW78cZ90qLBRHDR+IYdYu2mrrQCseef41npn6Lz6c+iJD9xrM1LffprComIxIhMPGHkj3Lh0Rv32FSqc0gT0TEYP/O+koCGToUmfKpVPHDrzw8quI744uKSmheU5OnZSuNf2JFdAlzmoXmEr5NTk8Bvbtzf29umv1xnW48swTuezuRzl3/z2IZUdJ1yTIiCc5u2cX/rbwB05v1opIWr/q55ZWcs+yH7khf4D+4XoeAdMgnUpx4th9kWYtIBwkUVWJpBLEggHKVyxmw6plLtvoYNkS4ml36zs1gOgWb/9WSq0XkZ7oVKrNWVtmsOnuWh/53z8E3CwiJyulVvpSdh+l1LTNHO8p4CoR+RqtX5+BXjwiIjF0F9zPlFJXbtdFsYO7czmJ6su/ev/N1JEHDKfqpwVcOOWvXPzA88RrahBD9CvftjFCGRjhKAQjTDhofy75vzMQ0yQvL48zTjuFqyb/mYv+dAY9OrbGSFQgqWrES+mTuPUVj8QnYm2LCtvQtfwK1q7lL3fczquvvEKzrMwGJkBt037m9Xc58YJrwAyCFQI7AsEohP6/vTMPr6o61/hv7fGMSQhhKgQBmUEQh4qogKhYtSoCgmKrola94nitXqyiFRXQim2pQ621itUrioLiWBUBcQKLIPNMwhhISEJyhn3O2Xuv+8c6ScCLFCxTIe/z7Iecfc6zz2bly9rf+tb3vq86RDCCCOUgglGMcJT7rhvKxNkLMUIhzFAAI2iTHwrQzLIRniSd8nBSHm0DQQY3bZZ9MAilvlTT1IRKtYSXYdglP2fq5EnkWDqvThgb973MaLkfDEKTaW+XYy9wFrBQKCPPd1F/WLVPDCHE+9l04YfctS7eqZz3R1TZ7SMhxA7gS/bcXfgAsBYoRtnOjZN19s+XoMqaw7NpS03q0mJv/lP7VSZTSvldJBKZOeuDd8/6oGybce8Fp/LRotUs21jKSQVN1KPeDIAVRFoBpG7SuHku57VsXSuVVZVwiEbCCF9DkxK0DAjBXya9zbaSEn5z1cCdlI/qrClE1s7trjtuY9Toh9leXkGf3mcon5Uay4os17BH9+PYEUsomtVOopTKqsNV6Y7vg55CeC7vf72Aj+cv49ZTu+CmVM48f1MZMSdDwvdIpT1sTZCjGbSLRhC6hmYaaIaO60sM06qLbOnTqX1rXpz8Fkvnz2XRvDkpz3Wf2R/jn97HOrWU8i5+eGGIlPL8773ek7uWRO1h7NbJazefT6Nsmv+fVbOU8iUUIfdHYb9rv8bj8TuefGXKgn+OvoGg9Fm8cRuX9u+dNdcJImylXYcZUjJfugFCx0XjoTGPYtoBqqqradOqNVdfMZSAFUZD0qxZM3RQUl2+j9A01f1W67GijIJCtsnj48aolEPKWpmCOk8W6Ni+HR07dsTXzdoFIpD1ZHFr/xXSQ0pJKpmkUSREqjJGsjTGN6u3MGPTNvqJPLbG0kigoaVMkDRDQ7N0dMtAs5SSay03s9aCDi4671yuu/H6ZMpxRkop94ot/q+wl7PzEY/9HtRSymXRgPX8g5M/Gf7gkHMCDfNyMMNhtKBKOaQdVocZQpoBKqrjrFy9ktdee53h115Hm/YdcX3JsqVLufM39/NfN1xPl2MLueCiAYh0HC27SET6qmmoVnZX1AW3l6F219f36jZoanLobM8JZiArI6wrnWrPBc1VNXHfR/g+6Xg13y5ewVOD+xFbs4U5S4qZXlTC6X4Om1IZ0r4kogsEOkITaJaOGTTQgzZJIBwMZLmZ6g9IChXcRcXFctu2bSVSyr/tr7Gv7/1QOCAq3SQrPzEAABBNSURBVLFU5p73Fq25TNhW4E93XIUI5yBCUQhGVUBbYT6bO59p739Io6ZNad26DfeNfgTDskm6Pq4Px7TryMjR43jl+T/zoYAbrrmSaDSMdJUUruobkdmqhlEnuVWzGSMlUONGm3XG8j01WWsi6/ViUbxpM1tKttKpUydyw0H1Wc9FpGO427fwwBPPcmX3djjrtzL3u2LeX7uZ3jKH9QkXx/MxNEFY13CFJGDomEETIxjADNq8tmA1A/v2RNgh9YQxlMdMWUU1d4y8NxmLxYZld8z2C/Z1oXik4oAEtZRyhxDiyo8Xrp6sR3IDWjgXwkpqK6MH+d0fn6Fp8xbc//BYMtkY9KQknVFe4p5f48alMexXIygpXsuY8RPQBfQ54zTOOPUUpXEt1Qy8dl0xY8b/gXGjH6CgYUPVV1Ljbeh7dTmy9OuCP+tE++LEl/hk+qcUFhbyy8uHcv6ZpyFSMWTVdp5+4VX6tGxE47jDwkUbmLp6A2fKPLYkMsRcHz87gIYGG/wUnXOjmGELMxJADwVYU7aDG9q2RgRCYNpIw8bXTa6//dZEOp35q5Ty6/057vuaUx+p2K/Vj50hpXzX9f0pt0x4JUU4D0J5lMTS3DnqIc4fMJCLh15BwpVUxhJMmTqF6pRLslZeQGloLF+xgjtvvhE9ksdNd93LjXfcTXXa56HHnuCBceNZtakU3wxjhqKEoznoVlDN3ECNir9wla3bsy++TOnWrVkfQze7M+nw25F3MfvTj+jcoR22LlT5MLGDb76eg4xXc1zAYntxKS8sXsO5WgPerC7ln5nqWuNmSxOEDI0VMokdNKkQPlY0xDvLirikzylowQgiEKolQbz85jty1udflCaTyX/bDPT7SKbdXY6jFQfUJCSWTN3w7mdzT3tu6kctevXuq//lpVcZef+DGMEw8YxP2pV8u2Ahfxr/KG27dKd54TGKme37eBI0wyYUjuALHceVmLrJKX36cUa/s8kkE7w56RU2b9pIYWEhN99yG7kFjZFeJmsi5Nbm0dVVO3ht2ge0ad6Y/r17gusgMibSUCLrQjO4/46bwEujJauQiSpe+3Am/9OrK1UrN/Hm0iLO0nOIOR74YGSrJZYGUV1gWkoO4Y2SLawJwq9P6sB3m7ZzxeWDEOEcVSq0QyxcuY5b7/5NMpFI/FxKmdzf412fUyuIOgLLAfoCIToGbHvekKFDQ2OemEDa12pn5LSnTItS8RgN8nLr+HwyK1Qj6yphWVGFXXiIpq44gNu2bGbmjOksXbyIB+8fRTRoqeanjIPIJBEZ5bUipGp7rWGnSEAItW2Opqut+NgOPv9sNmuXLeOc/BwqVm/l8dnfcbGXQ3E8Q1naw/EktiYosHUaRAzezmzn1u7tyWnegMatm/BWcQl9Tu9J1xNPROQ2RAZz2RbLcMLp/RLbSkuvl1K+cgDGWfZ/6vNdzn004nRkjYH6UYQDln7UQEq53Emlhkx9663kkpVrqHBcypMZKp0M8bSH50uiOTkYmur1CBoaEUsjauvk2DoRSyOY9YnRs78eJfHl47iSpOvToHEzBl/+C26+/U5G/fZBPPSd/GNkbRoinSTSSSCTcXDUIZNVEKuAqjLkjjLcim1M/scsLup0DK6jugo9JKapEzF1Glo6PwkaHBMxycu1eDtTzrUd29C4SS4FjXIJ5EQorozRpXMHCEWRgShx36Dfzy9xqqqr/3IgAroGruvvchytOOBBDSClfC+dSt95xYDzE8uL1rM9maE67amNCU3D1jUChiBoCsKGIGJARPeJ6h5RA6KmIGKq9y1NoAlln5HxJdXxJKWVO0h5kryCxlw4YCBvvfNOrV+LyNaqpasaovxUAukk8J24cspyEviJaryqclLl2/jrG+8x8MQO+GkXIQSGbdAhP4fFmkNB1KJ5jk3zPJsiO8UnXgW3dmtHqxb5BPPDWHlh5pZs54wenRHBMNhhHCzOvHBQakvJ1k8cx/nRFs17Azfj7XIcrThoxnuOk3zGDgQb3Dz4Z/fd/+LbwZaFLbLKSTphSyOg+Xwx4xPmfPlFLSNc+j6eLwmGwwy6dAjNWx1L2lN9wxlfKS38ftzDlG7byhNPP4cATjqlJ0+Me5hBF56Hou3u1KFT45RVU6/OEhZktq/60YlTeOmjL1k4bgTpqjhmJEgg3+WS41rzzpJi3ijbjq1pSE3Qu7CAIc0aYufY2LkhAvmq4Wn63GWMufVqhBUkJXXOunhIevXqNV/FYrFBsvaLDwy8fWSTCyG6AONRW9L5Uso9WhEcLDpX9v1nUX3U7YDh2V3GvcJBdZNMOckxph1g9FUX3Tvh1Wmh/E7tybU1vpz+IbNnTOfC/n155I5fqYpFDW1LKN25V6ZNZc2GLQwYNJhuJ51CytdIuZLLr76WyopK0p7MlqA1kk4aJyMJalnHL83gkb9OYujZvWjbtKFyms0aClHjLaDrXNrnZLaWlaOZBnrAwoyEANBMg0GREBcn04r8qwmErmHYJmbYxswJYeeGWVYRo2ObFhjhKPGMx8DhNyWXLl8xJx6Pn5vdFj6g+BGzcwbVwPQUiqr1gzjIdC6ABcAk4NF9vfBBST92RibljIlXVd5908D+ySVzZ/PEQ6MguYMn7rmFMzu0gJK1+JtW4W9cibdhBf6mlWjbitixeT0jrxnCmsULeHL8YwTwsHVBq1at6XTc8aQ9JQ6TzPgMGDKMx/8wAdcI4FthUsJmefFmNlUmEMGoalgKhhGBkLKkC4QRdoj2bVqR3yCP1/+5EhkMYefnEmycT6R5AdNKSynPtclp1YhoYUMizfMJNc0j2CiXQH4O6YDNy18v5pcX9KMskeH0y25MzFuw8N14PN7/YAQ0gOf5uxz/ClLKlVLKF1Cz7r9CXw4SnSt7b89IKWewl9rcO+OQ+P6mHOcpIcT6K4cOfn3cqLutgT07a3LLapYvWsI/vviG0vKKbM+Gj9Q0enZuy6ez53B+rx5ceUFf5q7cyMS/Pccvrr0RQwMHJamQ9pQuc/M27enR8zTue2gst4+4kcZ5jXnpuWfQ3KSSTHAzSD+jSJBkme1eBmlaPDjiKuYtXs5jH85Bei7tmxVwYuumzFq/lZyGeXTt2hTfU4wbIQSaaVDpeoydNpt7rx7Iwo1lDLj70Xh1PDHBSaXvlQe6vLQT3APb+7EnOtdH7ErnKkO5CzwNDPv+hfZA5xqwP270kJlZSynfEUKcNGrM4x++88bkJu0b5Zrt86L0bdaQBtGmeKkMvuchTYO/z1/GPRf1pkuzfEQmxck9uvHuzC/VdVDqomnPV+KKGUHKNuh4Qk8KW7fjmb9NJO0kOP9n59Lr5B7o2U0XpYntgS9Vqc9LI0IZcDP89LQCTu7ZEy/lsGptMd8sXka3zu1ZF8/w5FdLaNc0n4JIkKqEw4KiLaR9yf3XDmHK14vkPU+/kkg4qSullFMO9pi6mQOasu8NnWuElHILgBBiNFAshPjFbtYSe03n+jE4pA7tUsolQoguc5etnbh0tXHO2B7dwuWbk8yrqCZHaPwkHCT/2AYMP6kTLy9fR5/zf4Zvh/n71Pc4seepVKUl5UmXCselKuUSTyufcdtxqbB0gmaEgb+6FQufObM+4e3fjsE2DXqd8lP6nNaTcMjKEnhrekNqhN9Vl57uuXRs0JiO3Y9XpIFMiuodO1i1bj1l28spaFDAf/fuRUXc4Yqxf45/u2LdxoSTulhKueJQjGf1uu+Ib1z0g+8LIYahmvklMHsP7JfdYW/pXHVCiXV0rvtRkg8SGINaINbQucp2c61/Cwd882VvIYQYZCBebGkEAmeYuUa18OiYF+GyM7syz4RG7dtyzoCBfLZqC5/PX8rlN97O1liGrbEUr7/wLGWlpXhSULzoG6SX4aLr/5uTT+9LTsAgahtELYOgoWFIl8Xz5jJvzpcs/G4+64uK+M0999C9W1daFTZHz2plC9/LzuA1/dVqFie7iJXpNF7K4c+Tp8lRT050XM8bn0rvn2b/Hzl+sv3NU3c5t/LJS/Zq80UIcSywck/Vj6zp5/NSypY7nStGVUA+FkIsA66RUn61l/e7EbhKSjk9+3o00FZKOex7n5sNPHfYVj/2BCnlm0KImRtdZ/yrrjOkh51jn5sf0bSGOcxZWczvR5zD7BWb+XjOAoaN+DVrypMUVSTYXOkQ6d4fXZqkPB+jRVd0P8kO32ZzlUPSNVU/iesTtQzClk6Xn/aix6mnU7xmFZ/PnE6jwtZ8POsLNm3cgABMw6Bjhw6ccHw3WrcsRJMqwEV2NsfN8PH0Gdx230Ox7ZWVy+JJZ3iWuHpIsTeLw+8jKx1nqx+Fjer3393CdiYHic6VvS8T5b8oACt7b+m9WaMcNjP1zhBC9AgI7amQbXTv1qYw9NQjI/l2m0NReYI+l17N6u1Jlm+NsaqkmsqKJPFEGs/zMW2DQMgiEjJpELEoiNoURG3yQxa5gexsberYhsAQSrDd0JSjrq4paWBDE0jPZe3qlSxe8C0biosR+IRDIU7o0QMnkWDsuLGxNWvWbo/F43cCUw7mYnAPYyaPuXbSLueKn79sjzO1UP6G66jTrBBAkcyaDgkh3gc+qyG+CiG6o2TKOqGUWa+RUi7MvieA21DB3gzlpvWalPK+H/huC1UaHIySPRsnpfzjTu/PQNWpdx7bM+UPSKLtcu3D4PfxgxBC9MqNhH+Xdr2TTu3dVzvz8uuMTONOrN5UxcbNFZRs2Uom44MwMewgdiRAKGIRjtjkRmwKcmwaRizywxa5QZOAoWHpWtZFV/WPGJrAyAZ3zeu6vhIlH5yIVTF10v/KZyb8PhmLxcrj8dhIYNL+7IX+dyGEkIVX7fqE3jDxyqOy9+OwST92Bynll8BpQogOs2fN+PXMT6dfEWjQRGLnhYJdB5BJS3QrgBnOxY6Al45hmk1IWx5ORpFPY46LQJB2fco3rGHRzPeJ5ORxTPvOtO7QhbyGDTA1Ldt7otW67GaScRZ8NZt/TJmU+PqzT3XTtGbEY9WPATMPh5l5d/BS+73x7z8Sh/VM/X1k86rz0MzhSO9srIhrFLQLBZp2MsItjydV9AUt+19HKGoRjtrkhS2Clo5tKG9z5XGu4SarqdywitK1y0nHq2jT5Xjan3AKm1csomjJt3LJV7Oqi5YtDFiB4PxEddULwGS5D5p2hwJCCNl08J92OVfyxi1H5Uz9HxXUO0MIYQCnIrR+wrDPll6mhxDCshsWJsNNWmqhRj8JBvIa6VY4gmnZGJadLd9l8FMJ0tXlvlO+1anaut6t3FRkpBPVphUMr3Az6eluypkBfCql3C8lpoMBIYQsuOixXc6VTbu7Pqj/k5FdqDRB+WO3BZoKTW8uNL0Byqk3AKQRONL3q6XnbkJ5YRejFj3Fh1OOvK8QQsj88x7e5Vz5B/cdlUF9WOfU+4JsnluSPWYe2rs5NPDS9Tk1HEFBXQ/w3UOy73PYoT6ojyC49dUPoD6ojyj47kHpcD3sccQsFI92CCGKUE1FO6NYStnq4N/NoUV9UNfjiMNBZ77Uox4HGvVBXY8jDvVBXY8jDvVBXY8jDvVBXY8jDv8HKqPdJ2HqQiIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f173844f710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tfr.plot_topomap(tmin=tmin,tmax=tmax,ch_type='grad',mode='mean',baseline=(None,0));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Ref\n",
    "- https://mne-tools.github.io/stable/auto_examples/connectivity/plot_cwt_sensor_connectivity.html"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [default]",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.15"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}